Temperature Diagnostics¶
Once you copy this repository, feel free to delete this notebook!
Imports¶
# Display output of plots directly in Notebook
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import intake
import numpy as np
import pandas as pd
import xarray as xr
import ast
from ncar_jobqueue import NCARCluster
from distributed import Client
Spin up a Cluster¶
cluster = NCARCluster(memory='10 GB')
cluster.scale(20)
client = Client(cluster)
client
Client
Client-8f11c72f-1196-11ec-9cd4-3cecef1a526c
| Connection method: Cluster object | Cluster type: dask_jobqueue.PBSCluster |
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/mgrover/proxy/8787/status |
Cluster Info
PBSCluster
37f4e708
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/mgrover/proxy/8787/status | Workers: 0 |
| Total threads: 0 | Total memory: 0 B |
Scheduler Info
Scheduler
Scheduler-a1d073a1-2ca8-428c-8918-a469fc6b4259
| Comm: tcp://10.12.206.53:46532 | Workers: 0 |
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/mgrover/proxy/8787/status | Total threads: 0 |
| Started: Just now | Total memory: 0 B |
Workers
Data Ingest¶
# Read in the data using xarray or some other package
data_catalog = intake.open_esm_datastore('data/silver-linings-aws-year1.json', csv_kwargs={"converters": {"variables": ast.literal_eval}},)
Subset for 2m Temperature¶
data_subset = data_catalog.search(frequency='month_1')
Read in the dictionary of datasets¶
dsets = data_subset.to_dataset_dict(cdf_kwargs={'chunks':{'time':-1}})
--> The keys in the returned dictionary of datasets are constructed as follows:
'component.stream.case'
100.00% [1/1 00:00<00:00]
ds = xr.open_dataset(data_subset.df.path.values[0])
ds = dsets[list(dsets.keys())[0]]
ds
<xarray.Dataset>
Dimensions: (lat: 192, lon: 288, zlon: 1, nbnd: 2, lev: 70, ilev: 71, time: 12)
Coordinates:
* lat (lat) float64 -90.0 -89.06 -88.12 ... 89.06 90.0
* lon (lon) float64 0.0 1.25 2.5 ... 356.2 357.5 358.8
* zlon (zlon) float64 0.0
* lev (lev) float64 5.96e-06 9.827e-06 ... 976.3 992.6
* ilev (ilev) float64 4.5e-06 7.42e-06 ... 985.1 1e+03
* time (time) object 2035-02-01 00:00:00 ... 2036-01-01...
Dimensions without coordinates: nbnd
Data variables: (12/848)
gw (lat) float64 dask.array<chunksize=(192,), meta=np.ndarray>
zlon_bnds (zlon, nbnd) float64 dask.array<chunksize=(1, 2), meta=np.ndarray>
hyam (lev) float64 dask.array<chunksize=(70,), meta=np.ndarray>
hybm (lev) float64 dask.array<chunksize=(70,), meta=np.ndarray>
P0 float64 ...
hyai (ilev) float64 dask.array<chunksize=(71,), meta=np.ndarray>
... ...
soa5_c1SFWET (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
soa5_c2 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
soa5_c2DDF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
soa5_c2SFWET (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
wet_deposition_NHx_as_N (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
wet_deposition_NOy_as_N (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
Attributes:
host:
intake_esm_varname: ['date', 'datesec', 'date_written', 'time_writte...
source: CAM
Conventions: CF-1.0
initial_file: b.e21.BWSSP245cmip6.f09_g17.CMIP6-SSP2-4.5-WACCM...
logname: geostrat
topography_file: /scratch/geostrat/inputdata/atm/cam/topo/fv_0.9x...
time_period_freq: month_1
model_doi_url: https://doi.org/10.5065/D67H1H0V
case: b.e21.BW.f09_g17.SSP245-TSMLT-GAUSS-LOWER-0.5.001
intake_esm_dataset_key: atm.cam.h0.b.e21.BW.f09_g17.SSP245-TSMLT-GAUSS-L...xarray.Dataset
- lat: 192
- lon: 288
- zlon: 1
- nbnd: 2
- lev: 70
- ilev: 71
- time: 12
- lat(lat)float64-90.0 -89.06 -88.12 ... 89.06 90.0
- long_name :
- latitude
- units :
- degrees_north
array([-90. , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958, -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508, -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058, -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607, -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157, -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707, -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257, -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806, -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356, -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906, -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455, -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005, -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555, -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105, -10.837696, -9.895288, -8.95288 , -8.010471, -7.068063, -6.125654, -5.183246, -4.240838, -3.298429, -2.356021, -1.413613, -0.471204, 0.471204, 1.413613, 2.356021, 3.298429, 4.240838, 5.183246, 6.125654, 7.068063, 8.010471, 8.95288 , 9.895288, 10.837696, 11.780105, 12.722513, 13.664921, 14.60733 , 15.549738, 16.492147, 17.434555, 18.376963, 19.319372, 20.26178 , 21.204188, 22.146597, 23.089005, 24.031414, 24.973822, 25.91623 , 26.858639, 27.801047, 28.743455, 29.685864, 30.628272, 31.570681, 32.513089, 33.455497, 34.397906, 35.340314, 36.282723, 37.225131, 38.167539, 39.109948, 40.052356, 40.994764, 41.937173, 42.879581, 43.82199 , 44.764398, 45.706806, 46.649215, 47.591623, 48.534031, 49.47644 , 50.418848, 51.361257, 52.303665, 53.246073, 54.188482, 55.13089 , 56.073298, 57.015707, 57.958115, 58.900524, 59.842932, 60.78534 , 61.727749, 62.670157, 63.612565, 64.554974, 65.497382, 66.439791, 67.382199, 68.324607, 69.267016, 70.209424, 71.151832, 72.094241, 73.036649, 73.979058, 74.921466, 75.863874, 76.806283, 77.748691, 78.691099, 79.633508, 80.575916, 81.518325, 82.460733, 83.403141, 84.34555 , 85.287958, 86.230366, 87.172775, 88.115183, 89.057592, 90. ]) - lon(lon)float640.0 1.25 2.5 ... 356.2 357.5 358.8
- long_name :
- longitude
- units :
- degrees_east
array([ 0. , 1.25, 2.5 , ..., 356.25, 357.5 , 358.75])
- zlon(zlon)float640.0
- long_name :
- longitude
- units :
- degrees_east
- bounds :
- zlon_bnds
array([0.])
- lev(lev)float645.96e-06 9.827e-06 ... 976.3 992.6
- long_name :
- hybrid level at midpoints (1000*(A+B))
- units :
- hPa
- positive :
- down
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- formula_terms :
- a: hyam b: hybm p0: P0 ps: PS
array([5.960300e-06, 9.826900e-06, 1.620185e-05, 2.671225e-05, 4.404100e-05, 7.261275e-05, 1.197190e-04, 1.973800e-04, 3.254225e-04, 5.365325e-04, 8.846025e-04, 1.458457e-03, 2.404575e-03, 3.978250e-03, 6.556826e-03, 1.081383e-02, 1.789800e-02, 2.955775e-02, 4.873075e-02, 7.991075e-02, 1.282732e-01, 1.981200e-01, 2.920250e-01, 4.101675e-01, 5.534700e-01, 7.304800e-01, 9.559475e-01, 1.244795e+00, 1.612850e+00, 2.079325e+00, 2.667425e+00, 3.404875e+00, 4.324575e+00, 5.465400e+00, 6.872850e+00, 8.599725e+00, 1.070705e+01, 1.326475e+01, 1.635175e+01, 2.005675e+01, 2.447900e+01, 2.972800e+01, 3.592325e+01, 4.319375e+01, 5.167750e+01, 6.152050e+01, 7.375096e+01, 8.782123e+01, 1.033171e+02, 1.215472e+02, 1.429940e+02, 1.682251e+02, 1.979081e+02, 2.328286e+02, 2.739108e+02, 3.222419e+02, 3.791009e+02, 4.459926e+02, 5.246872e+02, 6.097787e+02, 6.913894e+02, 7.634045e+02, 8.208584e+02, 8.595348e+02, 8.870202e+02, 9.126445e+02, 9.361984e+02, 9.574855e+02, 9.763254e+02, 9.925561e+02]) - ilev(ilev)float644.5e-06 7.42e-06 ... 985.1 1e+03
- long_name :
- hybrid level at interfaces (1000*(A+B))
- units :
- hPa
- positive :
- down
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- formula_terms :
- a: hyai b: hybi p0: P0 ps: PS
array([4.500500e-06, 7.420100e-06, 1.223370e-05, 2.017000e-05, 3.325450e-05, 5.482750e-05, 9.039800e-05, 1.490400e-04, 2.457200e-04, 4.051250e-04, 6.679400e-04, 1.101265e-03, 1.815650e-03, 2.993500e-03, 4.963000e-03, 8.150651e-03, 1.347700e-02, 2.231900e-02, 3.679650e-02, 6.066500e-02, 9.915650e-02, 1.573900e-01, 2.388500e-01, 3.452000e-01, 4.751350e-01, 6.318050e-01, 8.291550e-01, 1.082740e+00, 1.406850e+00, 1.818850e+00, 2.339800e+00, 2.995050e+00, 3.814700e+00, 4.834450e+00, 6.096350e+00, 7.649350e+00, 9.550100e+00, 1.186400e+01, 1.466550e+01, 1.803800e+01, 2.207550e+01, 2.688250e+01, 3.257350e+01, 3.927300e+01, 4.711450e+01, 5.624050e+01, 6.680050e+01, 8.070142e+01, 9.494104e+01, 1.116932e+02, 1.314013e+02, 1.545868e+02, 1.818634e+02, 2.139528e+02, 2.517044e+02, 2.961172e+02, 3.483666e+02, 4.098352e+02, 4.821499e+02, 5.672244e+02, 6.523330e+02, 7.304459e+02, 7.963631e+02, 8.453537e+02, 8.737159e+02, 9.003246e+02, 9.249645e+02, 9.474323e+02, 9.675386e+02, 9.851122e+02, 1.000000e+03]) - time(time)object2035-02-01 00:00:00 ... 2036-01-...
- long_name :
- time
- bounds :
- time_bnds
array([cftime.datetime(2035, 2, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(2035, 3, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(2035, 4, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(2035, 5, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(2035, 6, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(2035, 7, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(2035, 8, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(2035, 9, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(2035, 10, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(2035, 11, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(2035, 12, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True), cftime.datetime(2036, 1, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True)], dtype=object)
- gw(lat)float64dask.array<chunksize=(192,), meta=np.ndarray>
- long_name :
- latitude weights
Array Chunk Bytes 1.50 kiB 1.50 kiB Shape (192,) (192,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - zlon_bnds(zlon, nbnd)float64dask.array<chunksize=(1, 2), meta=np.ndarray>
- long_name :
- zlon bounds
- units :
- degrees_east
Array Chunk Bytes 16 B 16 B Shape (1, 2) (1, 2) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - hyam(lev)float64dask.array<chunksize=(70,), meta=np.ndarray>
- long_name :
- hybrid A coefficient at layer midpoints
Array Chunk Bytes 560 B 560 B Shape (70,) (70,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - hybm(lev)float64dask.array<chunksize=(70,), meta=np.ndarray>
- long_name :
- hybrid B coefficient at layer midpoints
Array Chunk Bytes 560 B 560 B Shape (70,) (70,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - P0()float64...
- long_name :
- reference pressure
- units :
- Pa
array(100000.)
- hyai(ilev)float64dask.array<chunksize=(71,), meta=np.ndarray>
- long_name :
- hybrid A coefficient at layer interfaces
Array Chunk Bytes 568 B 568 B Shape (71,) (71,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - hybi(ilev)float64dask.array<chunksize=(71,), meta=np.ndarray>
- long_name :
- hybrid B coefficient at layer interfaces
Array Chunk Bytes 568 B 568 B Shape (71,) (71,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - date(time)int32dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- current date (YYYYMMDD)
Array Chunk Bytes 48 B 4 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type int32 numpy.ndarray - datesec(time)int32dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- current seconds of current date
Array Chunk Bytes 48 B 4 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type int32 numpy.ndarray - time_bnds(time, nbnd)objectdask.array<chunksize=(1, 2), meta=np.ndarray>
- long_name :
- time interval endpoints
Array Chunk Bytes 192 B 16 B Shape (12, 2) (1, 2) Count 36 Tasks 12 Chunks Type object numpy.ndarray - date_written(time)|S8dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type |S8 numpy.ndarray - time_written(time)|S8dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type |S8 numpy.ndarray - ndbase()int32...
- long_name :
- base day
array(0, dtype=int32)
- nsbase()int32...
- long_name :
- seconds of base day
array(0, dtype=int32)
- nbdate()int32...
- long_name :
- base date (YYYYMMDD)
array(20350101, dtype=int32)
- nbsec()int32...
- long_name :
- seconds of base date
array(0, dtype=int32)
- mdt()int32...
- long_name :
- timestep
- units :
- s
array(1800, dtype=int32)
- ndcur(time)int32dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- current day (from base day)
Array Chunk Bytes 48 B 4 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type int32 numpy.ndarray - nscur(time)int32dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- current seconds of current day
Array Chunk Bytes 48 B 4 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type int32 numpy.ndarray - co2vmr(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- co2 volume mixing ratio
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type float64 numpy.ndarray - ch4vmr(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- ch4 volume mixing ratio
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type float64 numpy.ndarray - n2ovmr(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- n2o volume mixing ratio
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type float64 numpy.ndarray - f11vmr(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- f11 volume mixing ratio
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type float64 numpy.ndarray - f12vmr(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- f12 volume mixing ratio
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type float64 numpy.ndarray - sol_tsi(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- total solar irradiance
- units :
- W/m2
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type float64 numpy.ndarray - f107(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- 10.7 cm solar radio flux (F10.7)
- units :
- 10^-22 W m^-2 Hz^-1
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type float64 numpy.ndarray - f107a(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- 81-day centered mean of 10.7 cm solar radio flux (F10.7)
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type float64 numpy.ndarray - f107p(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- Pervious day 10.7 cm solar radio flux (F10.7)
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type float64 numpy.ndarray - kp(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- Daily planetary K geomagnetic index
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type float64 numpy.ndarray - ap(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- Daily planetary A geomagnetic index
Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type float64 numpy.ndarray - nsteph(time)int32dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- current timestep
Array Chunk Bytes 48 B 4 B Shape (12,) (1,) Count 36 Tasks 12 Chunks Type int32 numpy.ndarray - ABSORB(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
- 1
- units :
- /m
- long_name :
- Aerosol absorption, day only
- cell_methods :
- time: mean
Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - ACTREL(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
- Micron
- long_name :
- Average Cloud Top droplet effective radius
- cell_methods :
- time: mean
Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - ADRAIN(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
- 1
- units :
- m
- long_name :
- Average rain Diameter
- cell_methods :
- time: mean
Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - ADSNOW(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
- 1
- units :
- m
- long_name :
- Average snow Diameter
- cell_methods :
- time: mean
Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - AEROD_v(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
- 1
- long_name :
- Total Aerosol Optical Depth in visible band
- cell_methods :
- time: mean
Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - ANRAIN(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
- 1
- units :
- m-3
- long_name :
- Average rain number conc
- cell_methods :
- time: mean
Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - ANSNOW(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
- 1
- units :
- m-3
- long_name :
- Average snow number conc
- cell_methods :
- time: mean
Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - AOA1(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
- 1
- units :
- kg/kg
- mixing_ratio :
- wet
- long_name :
- Age-of_air tracer 1
- cell_methods :
- time: mean
Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - AOA2(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
- 1
- units :
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - BRCL(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - BRO(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - BRONO2(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - BROX(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - BROY(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - BRY(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - BURDENSO4dn(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CCL4(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CCN3(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CDNUMC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CF2CLBR(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CF3BR(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CFC11(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CFC113(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CFC114(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CFC115(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CFC11STAR(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CFC11_CHML(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CFC12(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CFC12_CHML(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH2BR2(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH2O(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3BR(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3CCL3(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3CHO(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3CL(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3CN(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3CO3(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3COCH3(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3COCHO(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3COOH(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3COOOH(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3O2(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3OH(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH3OOH(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH4(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CH4_CHML(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CHBR3(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CL(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CL2(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CL2O2(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CLDHGH(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CLDICE(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CLDLIQ(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CLDLOW(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CLDMED(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CLDTOT(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CLO(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CLONO2(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CLOUD(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CLOX(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CLOY(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CLY(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CME(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CMFDQ(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CMFMC(time, ilev, lat, lon)float32dask.array<chunksize=(1, 71, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 179.72 MiB 14.98 MiB Shape (12, 71, 192, 288) (1, 71, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CMFMCDZM(time, ilev, lat, lon)float32dask.array<chunksize=(1, 71, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 179.72 MiB 14.98 MiB Shape (12, 71, 192, 288) (1, 71, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CO(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CO2(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CO2_CHML(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - COF2(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CONCLD(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CO_CHML(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - CO_CHMP(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - ISOP(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - ISOPNITB(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - ISOPNO3(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - IVOC(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - IVOC_CHML(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - KVH_CLUBB(time, ilev, lat, lon)float32dask.array<chunksize=(1, 71, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - LNO_COL_PROD(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - LNO_PROD(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - LWCF(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MASS(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MAXQ0(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_BCARY(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_BIGALK(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_BIGENE(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_C2H4(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_C2H5OH(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_C2H6(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_C3H6(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_C3H8(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_CH2O(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_CH3CHO(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_CH3COCH3(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_CH3COOH(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_CH3OH(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_CO(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_HCN(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_HCOOH(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_ISOP(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_MTERP(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEG_TOLUENE(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MEK(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MPAN(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MSKtem(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MTERP(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - MVK(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - N2O(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - N2O5(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - N2O_CHML(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NDEP(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NETDT(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NH3(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NH4(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NHDEP(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NH_5(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NH_50(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NITROP_PD(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NO(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NO2(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NO2_CLXF(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NO3(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NOX(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NOY(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NUMICE(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - NUMLIQ(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - PS(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - PSL(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - PS_12_COS(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - PS_12_SIN(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - PS_24_COS(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - PS_24_SIN(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - PTEQ(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - QRL_TOT(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - QRS(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - QRSC(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - QRS_TOT(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - QSNOW(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - RAD_ICE(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - RAD_LNAT(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - RAD_SULFC(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - RAINQM(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - RCO2_NO2_sum(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - REFF_AERO(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - RELHUM(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - RHREFHT(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - RO2(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - RO2_HO2_sum(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - RO2_NO3_sum(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - RO2_NO_sum(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - RO2_RO2_sum(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - ROOH(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - S(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SAD_AERO(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SAD_ICE(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SAD_LNAT(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SAD_SULFC(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SAD_TROP(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SF6(time, lev, lat, lon)float32dask.array<chunksize=(1, 70, 192, 288), meta=np.ndarray>
- mdims :
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Array Chunk Bytes 177.19 MiB 14.77 MiB Shape (12, 70, 192, 288) (1, 70, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFBCARY(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
- BCARY surface flux
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFBENZENE(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFBIGALK(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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- cell_methods :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFBIGENE(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFC2H2(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFC2H4(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFC2H5OH(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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- cell_methods :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFC2H6(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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- cell_methods :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFC3H6(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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- cell_methods :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFC3H8(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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- cell_methods :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFCH2O(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- cell_methods :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFCH3CHO(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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- cell_methods :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFCH3CN(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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- cell_methods :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFCH3COCH3(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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- cell_methods :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFCH3COCHO(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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- cell_methods :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFCH3COOH(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFCH3OH(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFCO(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
- CO surface flux
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFDMS(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFGLYALD(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFHCN(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFHCOOH(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFISOP(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFIVOC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFMEK(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFMTERP(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFNH3(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFNO(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFNO2(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFSO2(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFSVOC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFTOLUENE(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFXYLENES(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFbc_a4(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFdst_a1(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFdst_a2(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFdst_a3(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFncl_a1(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 36 Tasks 12 Chunks Type float32 numpy.ndarray - SFncl_a2(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- units :
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'MEG_CH3COCH3', 'MEG_CH3COOH', 'MEG_CH3OH', 'MEG_CO', 'MEG_HCN', 'MEG_HCOOH', 'MEG_ISOP', 'MEG_MTERP', 'MEG_TOLUENE', 'MEK', 'MPAN', 'MSKtem', 'MTERP', 'MVK', 'N2O', 'N2O5', 'N2O_CHML', 'NDEP', 'NETDT', 'NH3', 'NH4', 'NHDEP', 'NH_5', 'NH_50', 'NITROP_PD', 'NO', 'NO2', 'NO2_CLXF', 'NO3', 'NOX', 'NOY', 'NUMICE', 'NUMLIQ', 'NUMRAI', 'NUMSNO', 'O', 'O1D', 'O2', 'O3', 'O3_CHML', 'O3_CHMP', 'O3_Loss', 'O3_Prod', 'OCLO', 'OCNFRAC', 'OCS', 'OH', 'OMEGA', 'OMEGAT', 'ONITR', 'OddOx_CLOxBROx_Loss', 'OddOx_HOx_Loss', 'OddOx_Loss_Tot', 'OddOx_NOx_Loss', 'OddOx_Ox_Loss', 'OddOx_Prod_Tot', 'Op', 'PAN', 'PBLH', 'PDELDRY', 'PHIS', 'PM25', 'PO2', 'PRECC', 'PRECL', 'PRECSC', 'PRECSL', 'PRECT', 'PS', 'PSL', 'PS_12_COS', 'PS_12_SIN', 'PS_24_COS', 'PS_24_SIN', 'PTEQ', 'PTTEND', 'Q', 'QCO2', 'QFLX', 'QHC2S', 'QJOULE', 'QNO', 'QO3', 'QO3P', 'QRAIN', 'QREFHT', 'QRL', 'QRLC', 'QRLNLTE', 'QRL_TOT', 'QRS', 'QRSC', 'QRS_TOT', 'QSNOW', 'RAD_ICE', 'RAD_LNAT', 'RAD_SULFC', 'RAINQM', 'RCO2_NO2_sum', 'REFF_AERO', 'RELHUM', 'RHREFHT', 'RO2', 'RO2_HO2_sum', 'RO2_NO3_sum', 'RO2_NO_sum', 'RO2_RO2_sum', 'ROOH', 'S', 'SAD_AERO', 'SAD_ICE', 'SAD_LNAT', 'SAD_SULFC', 'SAD_TROP', 'SF6', 'SFBCARY', 'SFBENZENE', 'SFBIGALK', 'SFBIGENE', 'SFC2H2', 'SFC2H4', 'SFC2H5OH', 'SFC2H6', 'SFC3H6', 'SFC3H8', 'SFCH2O', 'SFCH3CHO', 'SFCH3CN', 'SFCH3COCH3', 'SFCH3COCHO', 'SFCH3COOH', 'SFCH3OH', 'SFCO', 'SFDMS', 'SFGLYALD', 'SFHCN', 'SFHCOOH', 'SFISOP', 'SFIVOC', 'SFMEK', 'SFMTERP', 'SFNH3', 'SFNO', 'SFNO2', 'SFSO2', 'SFSVOC', 'SFTOLUENE', 'SFXYLENES', 'SFbc_a4', 'SFdst_a1', 'SFdst_a2', 'SFdst_a3', 'SFncl_a1', 'SFncl_a2', 'SFncl_a3', 'SFnum_a1', 'SFnum_a2', 'SFnum_a3', 'SFpom_a4', 'SFso4_a1', 'SFso4_a2', 'SHFLX', 'SNOWHICE', 'SNOWHLND', 'SNOWQM', 'SO', 'SO2', 'SO2_CHML', 'SO2_CHMP', 'SO2_CLXF', 'SO2_XFRC', 'SO3', 'SOAG0', 'SOAG0_CHMP', 'SOAG1', 'SOAG1_CHMP', 'SOAG2', 'SOAG2_CHMP', 'SOAG3', 'SOAG3_CHMP', 'SOAG4', 'SOAG4_CHMP', 'SOLIN', 'SOLLD', 'SOLSD', 'SSAVIS', 'SST', 'ST80_25', 'SVOC', 'SVOC_CHML', 'SWCF', 'T', 'TAQ', 'TAUARDGBETAX', 'TAUARDGBETAY', 'TAUBLJX', 'TAUBLJY', 'TAUE', 'TAUGWX', 'TAUGWY', 'TAUN', 'TAUNET', 'TAUS', 'TAUW', 'TAUX', 'TAUY', 'TBRY', 'TCLY', 'TGCLDCWP', 'TGCLDIWP', 'TGCLDLWP', 'TH', 'THzm', 'TMDMS', 'TMOCS', 'TMQ', 'TMSO2', 'TMso4_a1', 'TMso4_a2', 'TMso4_a3', 'TOLUENE', 'TOTH', 'TOT_CLD_VISTAU', 'TREFHT', 'TREFHTMN', 'TREFHTMX', 'TROP_P', 'TROP_T', 'TROP_Z', 'TS', 'TSMN', 'TSMX', 'TTEND_TOT', 'TTGWORO', 'TTGWSDF', 'TTGWSDFORO', 'TTGWSKE', 'TTGWSKEORO', 'TTGWSPEC', 'TTPXMLC', 'T_12_COS', 'T_12_SIN', 'T_24_COS', 'T_24_SIN', 'U', 'U10', 'UTGWORO', 'UTGWSPEC', 'UU', 'UVzm', 'UWzm', 'U_12_COS', 'U_12_SIN', 'U_24_COS', 'U_24_SIN', 'Uzm', 'V', 'VD01', 'VEL_NAT2', 'VERT', 'VERTSRC', 'VQ', 'VT', 'VTGWORO', 'VTGWSPEC', 'VTHzm', 'VU', 'VV', 'V_12_COS', 'V_12_SIN', 'V_24_COS', 'V_24_SIN', 'Vzm', 'WD_BRONO2', 'WD_CH2O', 'WD_CH3CHO', 'WD_CH3CN', 'WD_CH3COCH3', 'WD_CH3COCHO', 'WD_CH3COOH', 'WD_CH3OH', 'WD_CH3OOH', 'WD_CLONO2', 'WD_GLYALD', 'WD_H2O2', 'WD_H2SO4', 'WD_HBR', 'WD_HCL', 'WD_HCN', 'WD_HCOOH', 'WD_HF', 'WD_HNO3', 'WD_HO2NO2', 'WD_HOBR', 'WD_HOCL', 'WD_HONITR', 'WD_NDEP', 'WD_NH3', 'WD_NH4', 'WD_NHDEP', 'WD_ONITR', 'WD_SO2', 'WD_SOAG0', 'WD_SOAG1', 'WD_SOAG2', 'WD_SOAG3', 'WD_SOAG4', 'WD_SVOC', 'WSUB', 'WTHzm', 'Wzm', 'Z3', 'ZMDQ', 'ZMDT', 'ZMMTT', 'ZMMU', 'bc_a1', 'bc_a1DDF', 'bc_a1SFWET', 'bc_a4', 'bc_a4DDF', 'bc_a4SFWET', 'bc_a4_CLXF', 'bc_c1', 'bc_c1DDF', 'bc_c1SFWET', 'bc_c4', 'bc_c4DDF', 'bc_c4SFWET', 'dgnumwet1', 'dgnumwet2', 'dgnumwet3', 'dry_deposition_NHx_as_N', 'dry_deposition_NOy_as_N', 'dst_a1', 'dst_a1DDF', 'dst_a1SFWET', 'dst_a2', 'dst_a2DDF', 'dst_a2SFWET', 'dst_a3', 'dst_a3DDF', 'dst_a3SFWET', 'dst_c1', 'dst_c1DDF', 'dst_c1SFWET', 'dst_c2', 'dst_c2DDF', 'dst_c2SFWET', 'dst_c3', 'dst_c3DDF', 'dst_c3SFWET', 'jcl2o2', 'jh2o2', 'jh2o_a', 'jh2o_b', 'jh2o_c', 'jno2', 'jo2_a', 'jo2_b', 'jo3_a', 'jo3_b', 'jpan', 'ncl_a1', 'ncl_a1DDF', 'ncl_a1SFWET', 'ncl_a2', 'ncl_a2DDF', 'ncl_a2SFWET', 'ncl_a3', 'ncl_a3DDF', 'ncl_a3SFWET', 'ncl_c1', 'ncl_c1DDF', 'ncl_c1SFWET', 'ncl_c2', 'ncl_c2DDF', 'ncl_c2SFWET', 'ncl_c3', 'ncl_c3DDF', 'ncl_c3SFWET', 'num_a1', 'num_a2', 'num_a2_sfnnuc1', 'num_a3', 'num_a4', 'num_c1', 'num_c2', 'num_c3', 'num_c4', 'pom_a1', 'pom_a1DDF', 'pom_a1SFWET', 'pom_a4', 'pom_a4DDF', 'pom_a4SFWET', 'pom_c1', 'pom_c1SFWET', 'pom_c4', 'pom_c4SFWET', 'r_GLYOXAL_aer', 'r_HO2_O3', 'r_N2O5_aer', 'r_NO2_aer', 'r_NO3_aer', 'r_O1D_H2O', 'r_OH_O', 'r_OH_O3', 'r_het1', 'r_het10', 'r_het11', 'r_het12', 'r_het13', 'r_het15', 'r_het16', 'r_het17', 'r_het2', 'r_het3', 'r_het4', 'r_het5', 'r_het6', 'r_het7', 'r_het8', 'r_het9', 'r_jsoa1_a1', 'r_jsoa1_a2', 'r_jsoa2_a1', 'r_jsoa2_a2', 'r_jsoa3_a1', 'r_jsoa3_a2', 'r_jsoa4_a1', 'r_jsoa4_a2', 'r_jsoa5_a1', 'r_jsoa5_a2', 'so4_a1', 'so4_a1DDF', 'so4_a1SFWET', 'so4_a1_CHMP', 'so4_a1_sfgaex1', 'so4_a2', 'so4_a2DDF', 'so4_a2SFWET', 'so4_a2_CHMP', 'so4_a2_sfgaex1', 'so4_a2_sfnnuc1', 'so4_a3', 'so4_a3DDF', 'so4_a3SFWET', 'so4_a3_sfgaex1', 'so4_c1', 'so4_c1AQH2SO4', 'so4_c1AQSO4', 'so4_c1DDF', 'so4_c1SFWET', 'so4_c2', 'so4_c2AQH2SO4', 'so4_c2AQSO4', 'so4_c2DDF', 'so4_c2SFWET', 'so4_c3', 'so4_c3AQH2SO4', 'so4_c3AQSO4', 'so4_c3DDF', 'so4_c3SFWET', 'soa1_a1', 'soa1_a1DDF', 'soa1_a1SFWET', 'soa1_a1_CHML', 'soa1_a1_sfgaex1', 'soa1_a2', 'soa1_a2DDF', 'soa1_a2SFWET', 'soa1_a2_CHML', 'soa1_a2_sfgaex1', 'soa1_c1', 'soa1_c1DDF', 'soa1_c1SFWET', 'soa1_c2', 'soa1_c2DDF', 'soa1_c2SFWET', 'soa2_a1', 'soa2_a1DDF', 'soa2_a1SFWET', 'soa2_a1_CHML', 'soa2_a1_sfgaex1', 'soa2_a2', 'soa2_a2DDF', 'soa2_a2SFWET', 'soa2_a2_CHML', 'soa2_a2_sfgaex1', 'soa2_c1', 'soa2_c1DDF', 'soa2_c1SFWET', 'soa2_c2', 'soa2_c2DDF', 'soa2_c2SFWET', 'soa3_a1', 'soa3_a1DDF', 'soa3_a1SFWET', 'soa3_a1_CHML', 'soa3_a1_sfgaex1', 'soa3_a2', 'soa3_a2DDF', 'soa3_a2SFWET', 'soa3_a2_CHML', 'soa3_a2_sfgaex1', 'soa3_c1', 'soa3_c1DDF', 'soa3_c1SFWET', 'soa3_c2', 'soa3_c2DDF', 'soa3_c2SFWET', 'soa4_a1', 'soa4_a1DDF', 'soa4_a1SFWET', 'soa4_a1_CHML', 'soa4_a1_sfgaex1', 'soa4_a2', 'soa4_a2DDF', 'soa4_a2SFWET', 'soa4_a2_CHML', 'soa4_a2_sfgaex1', 'soa4_c1', 'soa4_c1DDF', 'soa4_c1SFWET', 'soa4_c2', 'soa4_c2DDF', 'soa4_c2SFWET', 'soa5_a1', 'soa5_a1DDF', 'soa5_a1SFWET', 'soa5_a1_CHML', 'soa5_a1_sfgaex1', 'soa5_a2', 'soa5_a2DDF', 'soa5_a2SFWET', 'soa5_a2_CHML', 'soa5_a2_sfgaex1', 'soa5_c1', 'soa5_c1DDF', 'soa5_c1SFWET', 'soa5_c2', 'soa5_c2DDF', 'soa5_c2SFWET', 'wet_deposition_NHx_as_N', 'wet_deposition_NOy_as_N']
- source :
- CAM
- Conventions :
- CF-1.0
- initial_file :
- b.e21.BWSSP245cmip6.f09_g17.CMIP6-SSP2-4.5-WACCM.001.cam.i.2035-01-01-00000.nc
- logname :
- geostrat
- topography_file :
- /scratch/geostrat/inputdata/atm/cam/topo/fv_0.9x1.25_nc3000_Nsw042_Nrs008_Co060_Fi001_ZR_sgh30_24km_GRNL_c170103.nc
- time_period_freq :
- month_1
- model_doi_url :
- https://doi.org/10.5065/D67H1H0V
- case :
- b.e21.BW.f09_g17.SSP245-TSMLT-GAUSS-LOWER-0.5.001
- intake_esm_dataset_key :
- atm.cam.h0.b.e21.BW.f09_g17.SSP245-TSMLT-GAUSS-LOWER-0.5.001
Data Operation¶
First, we set up a few helper functions…
def area_grid(lat, lon):
"""
Calculate the area of each grid cell
Area is in square meters
Input
-----------
lat: vector of latitude in degrees
lon: vector of longitude in degrees
Output
-----------
area: grid-cell area in square-meters with dimensions, [lat,lon]
Notes
-----------
Based on the function in
https://github.com/chadagreene/CDT/blob/master/cdt/cdtarea.m
"""
from numpy import meshgrid, deg2rad, gradient, cos
from xarray import DataArray
xlon, ylat = meshgrid(lon, lat)
R = earth_radius(ylat)
dlat = deg2rad(gradient(ylat, axis=0))
dlon = deg2rad(gradient(xlon, axis=1))
dy = dlat * R
dx = dlon * R * cos(deg2rad(ylat))
area = dy * dx
xda = DataArray(
area,
dims=["lat", "lon"],
coords={"lat": lat, "lon": lon},
attrs={
"long_name": "area_per_pixel",
"description": "area per pixel",
"units": "m^2",
},
)
return xda
def earth_radius(lat):
'''
calculate radius of Earth assuming oblate spheroid
defined by WGS84
Input
---------
lat: vector or latitudes in degrees
Output
----------
r: vector of radius in meters
Notes
-----------
WGS84: https://earth-info.nga.mil/GandG/publications/tr8350.2/tr8350.2-a/Chapter%203.pdf
'''
from numpy import deg2rad, sin, cos
# define oblate spheroid from WGS84
a = 6378137
b = 6356752.3142
e2 = 1 - (b**2/a**2)
# convert from geodecic to geocentric
# see equation 3-110 in WGS84
lat = deg2rad(lat)
lat_gc = np.arctan( (1-e2)*np.tan(lat) )
# radius equation
# see equation 3-107 in WGS84
r = (
(a * (1 - e2)**0.5)
/ (1 - (e2 * np.cos(lat_gc)**2))**0.5
)
return r
def center_time(ds):
"""make time the center of the time bounds"""
ds = ds.copy()
attrs = ds.time.attrs
encoding = ds.time.encoding
try:
tb_name, tb_dim = _get_tb_name_and_tb_dim(ds)
ds['time'] = ds[tb_name].compute().mean(tb_dim).squeeze()
attrs['note'] = f'time recomputed as {tb_name}.mean({tb_dim})'
except AssertionError:
print('Using default time values')
ds.time.attrs = attrs
ds.time.encoding = encoding
return ds
def _get_tb_name_and_tb_dim(ds):
"""return the name of the time 'bounds' variable and its second dimension"""
assert 'bounds' in ds.time.attrs, 'missing "bounds" attr on time'
tb_name = ds.time.attrs['bounds']
assert tb_name in ds, f'missing "{tb_name}"'
tb_dim = ds[tb_name].dims[-1]
return tb_name, tb_dim
def calc_area_weighted_mean(ds, resample=True, sample_freq='AS'):
ds = center_time(ds.sortby('time'))
# Do some sort of calculation on the data
ds_out = (
(ds.resample(time=sample_freq).mean("time") * da_area).sum(dim=("lat", "lon"))
) / total_area
return ds_out
def convert_to_df(ds):
return ds.TREFHT.to_series().unstack().T
Compute the area for the weights¶
# area dataArray
da_area = area_grid(ds['lat'], ds['lon'])
# total area
total_area = da_area.sum(['lat','lon'])
Setup which variables to average¶
variables = ['TREFHT']
Run the computation on each dataset¶
xr.set_options(keep_attrs=True)
ds_list = []
for key in dsets.keys():
ds = dsets[key]
mean = calc_area_weighted_mean(ds, '1M')
out = mean[variables]
out.attrs['intake_esm_varname'] = variables
out.attrs['case'] = ds.case
ds_list.append(out)
Here we add additional case information¶
cases = []
for ds in ds_list:
cases.append(ds.case)
merged_ds = xr.concat(ds_list, dim='case')
merged_ds['case'] = cases
merged_ds.persist()
<xarray.Dataset>
Dimensions: (case: 1, time: 12)
Coordinates:
* time (time) object 2035-01-31 00:00:00 ... 2035-12-31 00:00:00
* case (case) <U49 'b.e21.BW.f09_g17.SSP245-TSMLT-GAUSS-LOWER-0.5.001'
Data variables:
TREFHT (case, time) float64 dask.array<chunksize=(1, 1), meta=np.ndarray>
Attributes:
intake_esm_varname: ['TREFHT']
case: b.e21.BW.f09_g17.SSP245-TSMLT-GAUSS-LOWER-0.5.001xarray.Dataset
- case: 1
- time: 12
- time(time)object2035-01-31 00:00:00 ... 2035-12-...
array([cftime.DatetimeNoLeap(2035, 1, 31, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2035, 2, 28, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2035, 3, 31, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2035, 4, 30, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2035, 5, 31, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2035, 6, 30, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2035, 7, 31, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2035, 8, 31, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2035, 9, 30, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2035, 10, 31, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2035, 11, 30, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2035, 12, 31, 0, 0, 0, 0, has_year_zero=True)], dtype=object) - case(case)<U49'b.e21.BW.f09_g17.SSP245-TSMLT-G...
array(['b.e21.BW.f09_g17.SSP245-TSMLT-GAUSS-LOWER-0.5.001'], dtype='<U49')
- TREFHT(case, time)float64dask.array<chunksize=(1, 1), meta=np.ndarray>
- units :
- K
- long_name :
- Reference height temperature
- cell_methods :
- time: mean
Array Chunk Bytes 96 B 8 B Shape (1, 12) (1, 1) Count 12 Tasks 12 Chunks Type float64 numpy.ndarray
- intake_esm_varname :
- ['TREFHT']
- case :
- b.e21.BW.f09_g17.SSP245-TSMLT-GAUSS-LOWER-0.5.001
Use datetime index instead of cftime¶
datetimeindex = merged_ds.indexes['time'].to_datetimeindex()
merged_ds['time'] = datetimeindex
Save the dataset¶
merged_ds.to_netcdf('data/global_average_temperature_aws_year1.nc')